Recommender systems

Assumed audience:

People who have not heard about recommender systems yet

Not my core area, but I need a landing page to refer to because this is coming up in conversations a lot at the moment.

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Recommender systems are a weird area. There have been some high profile examples of recommender models going back a long time (e.g. the Netflix Prize) and they are clearly commercially important. At time of writing, though, there is relatively little material introducing these systems to wide audiences, compared to say linear regressions and image classifiers and so on. Possibly this is something about who owns the data to make these tools go, which is to say, there are relatively few open data sets for recommender systems?

I would like to refer to a simple and good introduction to the area, but I do not know one, so what follows a scruffy word salad with some helpful links.

YouTube’s defence of their recommender system introduces in vague outline a lot of the features that make it go on the way to defending them from allegations of feeding extremism [citation needed].


Starting from an overview by Javier we can find many approaches to more abstract recommender systems.

  1. Most Popular recommendations (the baseline)
  2. Item-User similarity based recommendations
  3. kNN Collaborative Filtering recommendations
  4. Gradient Boosting Machine based recommendations
  5. Non-Negative Matrix Factorization recommendations
  6. Factorization Machines (Rendle 2010)
  7. Field Aware Factorization Machines (Juan et al. 2016)
  8. Deep Learning based recommendations (Wide and Deep, Cheng et al. (2016))
  9. Neural Collaborative Filtering (He et al. 2017)
  10. Neural Graph Collaborative Filtering (Xiang Wang et al. 2019)
  11. Variational Autoencoders for Collaborative Filtering (Liang et al. 2018)

Not mentioned in that list filtering based on monitoring the brains of subjects (Davis III, SpapΓ©, and Ruotsalo 2021).

A different take is in Microsoft’s Best Practices on Recommendation Systems, which breaks things down more along the lines of packages and implementations; probably what you want if you are an implementer not a researcher.

This area is moving fast, especially recently. I have the suspicion that the neural network methods are speciating at the moment.

Quentin Bacquet curtly summarises some methods and their performance on a problem, then introduces a VAE method via an implementation.

Ben Lorica in Questioning the Efficacy of Neural Recommendation Systems interviews Paolo Cremonesi and Maurizio Ferrari Dacrema, who are the authors of some recent review papers (Dacrema, Cremonesi, and Jannach 2019; Dacrema et al. 2021).

I am most familiar with the matrix factorization approaches (Koren, Bell, and Volinsky 2009; Yu et al. 2014, 2012; Lim and Teh 2007), especially NNMF) but there are many, e.g. variational autoencoder approaches are en vogue.

(Cheng et al. 2016) feels overcomplicated, but at least it is documented, and comes with an implementation: jrzaurin/pytorch-widedeep: A flexible package to combine tabular data with text and images using Wide and Deep models in Pytorch. Note that the author of that package advises using gradient boosting machines to get this job done.

Wu et al. (2021) taxonomises recommendation systems using Graph NNs, which is another natural idea. Their list goes a long way beyond Xiang Wang et al. (2019).


Cremonesi and Dacrema maintain reproduction code for several popular algorithms which is probably pedagogically useful.

Microsoft Recommenders, already mentioned, includes basic wrappers for a lot of different tools, and also includes worked examples and deployment advice. Maybe start there.

torch has gone large here recently I think? At least if Introduction to TorchRec is anything to go by.

Vowpal wabbit

Vowpal wabbit implements some classic recommendation systems extremely efficiently, but the manual has historically been famously abstruse. Now it is merely gruff. There are, I think(?), two tutorials in the documentation:

There is a worked example, vowpal_wabbit_deep_dive.ipynb in Microsoft Recommenders.

For seduction and addiction of humans

Popular choice. extra material at clickbait bandits and recommender dynamics.


Abernethy, Jacob, Francis Bach, Theodoros Evgeniou, and Jean-Philippe Vert. 2009. β€œA New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization.” Journal of Machine Learning Research 10 (Mar): 803–26.
Argyriou, Andreas, Miguel GonzΓ‘lez-Fierro, and Le Zhang. 2020. β€œMicrosoft Recommenders: Best Practices for Production-Ready Recommendation Systems.” In Companion Proceedings of the Web Conference 2020, 50–51. New York, NY, USA: Association for Computing Machinery.
Brouwer, Thomas Alexander. 2017. β€œBayesian Matrix Factorisation: Inference, Priors, and Data Integration.”
Chen, Dawei, Lexing Xie, Aditya Krishna Menon, and Cheng Soon Ong. 2017. β€œStructured Recommendation.” arXiv:1706.09067 [Cs], June.
Cheng, Heng-Tze, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, et al. 2016. β€œWide & Deep Learning for Recommender Systems.” arXiv:1606.07792 [Cs, Stat], June.
Dacrema, Maurizio Ferrari, Simone Boglio, Paolo Cremonesi, and Dietmar Jannach. 2021. β€œA Troubling Analysis of Reproducibility and Progress in Recommender Systems Research.” ACM Transactions on Information Systems 39 (2): 1–49.
Dacrema, Maurizio Ferrari, Paolo Cremonesi, and Dietmar Jannach. 2019. β€œAre We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches.” In Proceedings of the 13th ACM Conference on Recommender Systems, 101–9. Copenhagen Denmark: ACM.
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Koren, Yehuda, Robert Bell, and Chris Volinsky. 2009. β€œMatrix Factorization Techniques for Recommender Systems.” Computer 42 (8): 30–37.
Li, Lihong, Wei Chu, John Langford, and Xuanhui Wang. 2011. β€œUnbiased Offline Evaluation of Contextual-Bandit-Based News Article Recommendation Algorithms.” In Proceedings of the Fourth International Conference on Web Search and Web Data Mining (WSDM-11), 297–306.
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β€”β€”β€”. 2014. β€œParallel Matrix Factorization for Recommender Systems.” Knowledge and Information Systems 41 (3): 793–819.
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